The design and analysis of stochastic cost-effectiveness studies for the evaluation of health care interventions

Many clinical trials are in progress which involve the collection of patient-level data on both the health outcome and resource use consequences of the health care interventions under evaluation. The overall aim of many such evaluations will be to undertake a cost-effectiveness analysis, which will often result in a cost-effectiveness ratio summarising the value for money of the intervention in question. In this paper, we explore the issues surrounding the design and analysis of such studies. At the design stage of an analysis, we propose an improved sample size formula for cost-effectiveness analysis that allows for covariance between cost and effect differences. This approach is based on the 'net benefits' approach to the analysis of uncertainty in cost-effectiveness analysis. At the analysis stage of an evaluation, we explore the differences and similarities of the 'net benefit' approach to analysing cost-effectiveness information and the traditional approach based on cost-effectiveness ratios. Despite the apparent differences, we show that the two approaches are exactly equivalent when it comes to estimating the probability that the intervention is cost-effective under alternative values of the ceiling cost-effectiveness ratio appropriate for decision-making purposes.